Locations

We present an indoor tracking system based on two wearable inertial
measurement units for tracking in home and workplace environments. It applies
simultaneous localization and mapping with user actions as landmarks,
themselves recognized by the wearable sensors. The approach is thus fully
wearable and no pre-deployment effort is required. We identify weaknesses of
past approaches and address them by introducing heading drift compensation,
stance detection adaptation, and ellipse landmarks. Furthermore, we present an
environment-independent parameter set that allows for robust tracking in
daily-life scenarios. We assess the method on a dataset with five participants
in different home and office environments, totaling 8.7h of daily routines and
2500m of travelled distance. This dataset is publicly released. The main
outcome is that our algorithm converges 87% of the time to an accurate
approximation of the ground truth map (0.52m mean landmark positioning error)
in scenarios where previous approaches fail.

In this paper we present a full-scaled real-time monocular SLAM using only a
wearable camera. Assuming that the person is walking, the perception of the
head oscillatory motion in the initial visual odometry estimate allows for the
computation of a dynamic scale factor for static windows of N camera poses.
Improving on this method we introduce a consistency test to detect non-walking
situations and propose a sliding window approach to reduce the delay in the
update of the scaled trajectory. We evaluate our approach experimentally on a
unscaled visual odometry estimate obtained with a wearable camera along a path
of 886 m. The results show a significant improvement respect to the initial
unscaled estimate with a mean relative error of 0.91% over the total trajectory
length.

Activity recognition

Physical activity monitoring has recently become an important topic in
wearable computing, motivated by e.g. healthcare applications. However, new
benchmark results show that the difficulty of the complex classification
problems exceeds the potential of existing classifiers. Therefore, this paper
proposes the ConfAdaBoost.M1 algorithm. The proposed algorithm is a variant of
the AdaBoost.M1 that incorporates well established ideas for confidence based
boosting. The method is compared to the most commonly used boosting methods
using benchmark datasets from the UCI machine learning repository and it is
also evaluated on an activity recognition and an intensity estimation problem,
including a large number of physical activities from the recently released
PAMAP2 dataset. The presented results indicate that the proposed
ConfAdaBoost.M1 algorithm significantly improves the classification performance
on most of the evaluated datasets, especially for larger and more complex
classification tasks.

The new method proposed here recognizes activities performed by a group of
users (e.g., attending a meeting, playing sports, and participating in a party)
by using sensor data obtained from the users. Note that such group activities
(GAs) have characteristics that differ from those of single user activities.
For example, the number of users who participate in a GA is different for each
activity. The number of meeting participants, for instance, may sometimes be
different for each meeting. Also, a user may play different roles (e.g.,
'moderator' and 'presenter' roles) in meetings on different days. We introduce
the notion of role into our GA recognition model and try to capture the
intrinsic characteristics of GAs with a hybrid unsupervised/supervised
approach.

Personalization of activity recognition has become a topic of interest
recently. This paper presents a novel concept, using a set of classifiers as
general model, and retraining only the weight of the classifiers with new
labeled data from a previously unknown subject. Experiments with different
methods based on this concept show that it is a valid approach for
personalization. An important benefit of the proposed concept is its low
computational cost compared to other approaches, making it also feasible for
mobile applications. Moreover, more advanced classifiers (e.g. boosted decision
trees) can be combined with the new concept, to achieve good performance even
on complex classification tasks. Finally, a new algorithm is introduced based
on the proposed concept, which outperforms existing methods, thus further
increasing the performance of personalized applications.

Activity recognition has recently gained a lot of interest and there already
exist several methods to detect human activities based on wearable sensors.
Most of the existing methods rely on a database of labelled activities that is
used to train an offline activity recognition system. This paper presents an
approach to build an online activity recognition system that do not require any
a priori labelled data. The system incrementally learns activities by actively
querying the user for labels. To choose when the user should be queried, we
compare a method based on random sampling and another that uses a Growing
Neural Gas (GNG). The use of GNG helps reducing the number of user queries by
20% to 30%.

Ins and outs

An eartip made of conductive rubber that also realizes bio-potential
electrodes is proposed for a daily-use earphone-based eye gesture input
interface. Several prototypes, each with three electrodes to capture
Electrooculogram (EOG), are implemented on earphones and examined. Experiments
with one subject over a 10 day period reveal that all prototypes capture EOG
similarly but they differ as regards stability of the baseline and the presence
of motion artifacts. Another experiment conducted on a simple eye-controlled
application with six subjects shows that the proposed prototype minimizes
motion artifacts and offers good performance. We conclude that conductive
rubber with mixed Ag filler is the most suitable setup for daily-use.

This paper presents the design and implementation of a wearable oral sensory
system that recognizes human oral activities, such as chewing, drinking,
speaking, and coughing. We conducted an evaluation of this oral sensory system
in a laboratory experiment involving 8 participants. The results show 93.8%
oral activity recognition accuracy when using a person-dependent classifier and
59.8% accuracy when using a person-independent classifier.

This report proposes a thermal media system, ThermOn, which enables users to
feel dynamic hot and cold sensations on their body corresponding to the sound
of music. Thermal sense plays a significant role in the human recognition of
environments and influences human emotions. By employing thermal sense in the
music experience, which also greatly affects human emotions, we have
successfully created a new medium with an unprecedented emotional experience.
With ThermOn, a user feels enhanced excitement and comfort, among other
responses. For the initial prototype, headphone-type interfaces were
implemented using a Peltier device, which allows users to feel thermal stimuli
on their ears. Along with the hardware, a thermal-stimulation model that takes
into consideration the characteristics of human thermal perception was
designed. The prototype device was verified using two methods: the
psychophysical method, which measures the skin potential response and the
psychometric method using a Likert-scale questionnaire and open-ended
interviews. The experimental results suggest that ThermOn (a) changes the
impression of music, (b) provides comfortable feelings, and (c) alters the
listener's ability to concentrate on music in the case of a rock song.
Moreover, these effects were shown to change based on the methods with which
thermal stimuli were added to music (such as temporal correspondence) and on
the type of stimuli (warming or cooling). From these results, we have concluded
that the ThermOn system has the potential to enhance the emotional experience
when listening to music.

In this paper we describe a novel method for detecting bends and folds in
fabric structures. Bending and folding can be used to detect human joint angles
directly, or to detect possible errors in the signals of other joint-movement
sensors due to fabric folding. Detection is achieved through measuring changes
in the resistance of a complex stitch, formed by an industrial coverstitch
machine using an un-insulated conductive yarn, on the surface of the fabric. We
evaluate self-intersecting folds which cause short-circuits in the sensor,
creating a quasi-binary resistance response, and non-contact bends, which
deform the stitch structure and result in a more linear response. Folds and
bends created by human movement were measured on the dorsal and lateral knee of
both a robotic mannequin and a human. Preliminary results are promising. Both
dorsal and lateral stitches showed repeatable characteristics during testing on
a mechanical mannequin and a human.

Context and awareness

We propose an activity and context recognition method where the user carries
a neck-worn receiver comprising a microphone, and small speakers on his wrists
that generate ultrasounds. The system recognizes gestures on the basis of the
volume of the received sound and the Doppler effect. The former indicates the
distance between the neck and wrists, and the later indicates the speed of
motions. Thus, our approach substitutes the wired or wireless communication
typically required in body area motion sensing networks by ultrasounds. Our
system also recognizes the place where the user is in and the people who are
near the user by ID signals generated from speakers placed in rooms and on
people. The strength of the approach is that, for offline recognition, a simple
audio recorder can be used for the receiver. We evaluate the approach in one
scenario on nine gestures/activities with 10 users. Evaluation results
confirmed that when there was no environmental sound generated from other
people, the recognition rate was 87% on average. When there was environmental
sound generated from other people, we compare approach ultrasound-based
recognition which uses only the feature value of ultrasound against standard
approach, which uses feature value of ultrasound and environmental sound.
Results for the proposed approach are 65%, for the standard approach are 57%.

On preserving statistical characteristics of accelerometry data using their
empirical cumulative distribution

The majority of activity recognition systems in wearable computing rely on a
set of statistical measures, such as means and moments, extracted from short
frames of continuous sensor measurements to perform recognition. These features
implicitly quantify the distribution of data observed in each frame. However,
feature selection remains challenging and labour intensive, rendering a more
generic method to quantify distributions in accelerometer data much desired. In
this paper we present the ECDF representation, a novel approach to preserve
characteristics of arbitrary distributions for feature extraction, which is
particularly suitable for embedded applications. In extensive experiments on
six publicly available datasets we demonstrate that it outperforms common
approaches to feature extraction across a wide variety of tasks.

Reliable smartphone app prediction can strongly benefit both users and phone
system performance alike. However, real-world smartphone app usage behavior is
a complex phenomena driven by a number of competing factors. In this paper, we
develop an app usage prediction model that leverages three key everyday factors
that affect app usage decisions -- (1) intrinsic user app preferences and user
historical patterns; (2) user activities and the environment as observed
through sensor-based contextual signals; and, (3) the shared aggregate patterns
of app behavior that appear in various user communities. While rapid progress
has been made recently in smartphone app prediction, existing prediction models
tend to focus on only one of these factors. We evaluate a multi-faceted
approach to prediction using (1) a 3-week 35-user field trial, along with (2)
analysis of app usage logs of 4,606 smartphone users worldwide. We find our app
usage model can not only produce more robust app predictions than conventional
techniques, but it can also enable significant smartphone system optimizations.

In this paper, we introduce a wearable partner agent, that makes physical
contacts corresponding to the user's clothing, posture, and detected contexts.
Physical contacts are generated by combining haptic stimuli and anthropomorphic
motions of the agent. The agent performs two types of the behaviors: a) it
notifies the user of a message by patting the user's arm and b) it generates
emotional expression by strongly enfolding the user's arm. Our experimental
results demonstrated that haptic communication from the agent increases the
intelligibility of the agent's messages and familiar impressions of the agent.

Working dogs have improved the lives of thousands of people. However,
communication between human and canine partners is currently limited. The main
goal of the FIDO project is to research fundamental aspects of wearable
technologies to support communication between working dogs and their handlers.
In this pilot study, the FIDO team investigated on-body interfaces for
assistance dogs in the form of wearable technology integrated into assistance
dog vests. We created four different sensors that dogs could activate (based on
biting, tugging, and nose gestures) and tested them on-body with three
assistance-trained dogs. We were able to demonstrate that it is possible to
create wearable sensors that dogs can reliably activate on command.

Don't mind me touching my wrist: a case study of interacting with on-body
technology in public

Wearable technology, specifically e-textiles, offers the potential for
interacting with electronic devices in a whole new manner. However, some may
find the operation of a system that employs non-traditional on-body
interactions uncomfortable to perform in a public setting, impacting how
readily a new form of mobile technology may be received. Thus, it is important
for interaction designers to take into consideration the implications of
on-body gesture interactions when designing wearable interfaces. In this study,
we explore the third-party perceptions of a user's interactions with a wearable
e-textile interface. This two-prong evaluation examines the societal
perceptions of a user interacting with the textile interface at different
on-body locations, as well as the observer's attitudes toward on-body
controller placement. We performed the study in the United States and South
Korea to gain cultural insights into the perceptions of on-body technology
usage.

Sensing group proximity dynamics of firefighting teams using smartphones

Firefighters work in dangerous and unfamiliar situations under a high degree
of time pressure and thus team work is of utmost importance. Relying on trained
automatisms, firefighters coordinate their actions implicitly by observing the
actions of their team members. To support training instructors with objective
mission data, we aim to automatically detect when a firefighter is in-sight
with other firefighters and to visualize the proximity dynamics of firefighting
missions. In our approach, we equip firefighters with smartphones and use the
built-in ANT protocol, a low-power communication radio, to measure proximity to
other firefighters. In a second step, we cluster the proximity data to detect
moving sub-groups. To evaluate our method, we recorded proximity data of 16
professional firefighting teams performing a real-life training scenario. We
manually labeled six training sessions, involving 51 firefighters, to obtain 79
minutes of ground truth data. On average, our algorithm assigns each group
member to the correct ground truth cluster with 80% accuracy. Considering
height information derived from atmospheric pressure signals increases group
assignment accuracy to 95%.

EyeWear computing

3D from looking: using wearable gaze tracking for hands-free and
feedback-free object modelling

This paper presents a method for estimating the 3D shape of an object being
observed using wearable gaze tracking. Starting from a sparse environment map
generated by a simultaneous localization and mapping algorithm (SLAM), we use
the gaze direction positioned in 3D to extract the model of the object under
observation. By letting the user look at the object of interest, and without
any feedback, the method determines 3D point-of-regards by back-projecting the
user's gaze rays into the map. The 3D point-of-regards are then used as seed
points for segmenting the object from captured images and the calculated
silhouettes are used to estimate the 3D shape of the object. We explore methods
to remove outlier gaze points that result from the user saccading to non object
points and methods for reducing the error in the shape estimation. Being able
to exploit gaze information in this way, enables the user of wearable gaze
trackers to be able to do things as complex as object modelling in a hands-free
and even feedback-free manner.

I know what you are reading: recognition of document types using mobile eye
tracking

Reading is a ubiquitous activity that many people even perform in transit,
such as while on the bus or while walking. Tracking reading enables us to gain
more insights about expertise level and potential knowledge of users -- towards
a reading log tracking and improve knowledge acquisition. As a first step
towards this vision, in this work we investigate whether different document
types can be automatically detected from visual behaviour recorded using a
mobile eye tracker. We present an initial recognition approach that combines
special purpose eye movement features as well as machine learning for document
type detection. We evaluate our approach in a user study with eight
participants and five Japanese document types and achieve a recognition
performance of 74% using user-independent training.

We propose an eyeglass-based videophone that enables the wearer to make a
video call without holding a phone (that is to say hands-free) in the mobile
environment. The glasses have 4 (or 6) fish-eye cameras to widely capture the
face of the wearer and the images are fused to yield 1 frontal face image. The
face image is also combined with the background image captured by a
rear-mounted camera; the result is a self-portrait image without holding any
camera device at arm's length. Simulations confirm that 4 fish-eye cameras with
250-degree field of view (or 6 cameras with 180-degree field of view) can cover
83% of the frontal face. We fabricate a 6 camera prototype, and confirm the
possibility of generating the self-portrait image. This system suits not only
hands-free videophones but also other applications like visual life logging and
augmented reality use.

Joint ISWC/UbiComp keynote

Google's Glass has captured the world's imagination, with new articles
speculating on it almost every day. Yet, why would consumers want a wearable
computer in their everyday lives? For the past 20 years, my teams have been
creating living laboratories to discover the most compelling reasons. In the
process, we have investigated how to create interfaces for technology which are
designed to be "there when you need it, gone when you don't." This talk will
attempt to articulate the most valuable lessons we have learned, including some
design principles for creating "microinteractions" to fit a user's lifestyle.

ISWC posters

Activity monitoring in daily life as an outcome measure for surgical pain
relief intervention using smartphones

We investigate the potential of a smartphone to measure a patient's change
in physical activity before and after a surgical pain relief intervention. We
show feasibility for our smartphone system providing physical activity from
acceleration, barometer and location data to measure the intervention's
outcome. In a single-case study, we monitored a pain patient carrying the
smartphone before and after a surgical intervention over 26 days. Results
indicate significant changes before and after intervention, particularly in
physical activity in the home environment.

The aim of this study was to develop a reversible electrical contacting
through adhesive bonded neodymium magnets. To implement this, suitable magnets
and adhesives are chosen by defined requirements and conductive bonds between
textile and magnet are optimized. For the latter, three different bonds are
produced and tested in terms of achievable conductivity and mechanical
strength. It is shown that gold-coated neodymium magnets are most appropriate
for such a contact. The reproducible electrical resistances are low with
sufficient mechanical strength.

In this paper we propose FIREMAN, a low cost system for online monitoring of
firefighters ventilation patterns when using Self-Contained Breathing Apparatus
(SCBA), based on a specific hardware device attached to SCBA and a Smartphone
application. The system implementation allows the detection of relevant
ventilation patterns while providing feasible and accurate estimation of SCBA
air consumption.

We explore the use of Dielectric Elastomer (DE) micro-generators as a means
to scavenge energy from foot-strikes and power wearable systems. While they
exhibit large energy densities, DEs must be closely controlled to maximize the
energy they transduce. Towards this end, we propose a DE micro-generator array
configuration that enhances transduction efficiency, and the use of foot
pressure sensors to realize accurate control of the individual DEs. Statistical
techniques are applied to customize performance for a user's gait and enable
energy-optimized adaptive online control of the system. Simulations based on
experimentally collected foot pressure datasets, empirical characterization of
DE mechanical behavior and a detailed model of DE electrical behavior show that
the proposed system can achieve between 45 and 66mJ per stride.

E-textile practitioners have improvised innovatively with existing off-the
shelf electronics to make them textile-compatible. However, there is a need to
further the development of soft materials or parts that could replace regular
electronics in a circuit. As a starting point, we look at the possibility of
creating a repository of specific motifs with different resistance values that
can be easily incorporated into e-embroidery projects and used instead of
normal resistors. The paper describes our larger objective and gives an
overview of the first experiment done to compare the resistance values of a
simple pattern embroidered multiple times with conductive yarn to observe its
behavior and reliability.

This work discusses ways of measuring particulate matter with mobile
devices. Solutions using a dedicated sensor device are presented along with a
novel method of retrofitting a sensor to a camera phone without need for
electrical modifications. Instead, the flash and camera of the phone are used
as light source and receptor of an optical dust sensor respectively.
Experiments to evaluate the accuracy are presented.

We explore the feasibility of utilizing large, crowd-generated online
repositories to construct prior knowledge models for high-level activity
recognition. Towards this, we mine the popular location-based social network,
Foursquare, for geo-tagged activity reports. Although unstructured and noisy,
we are able to extract, categorize and geographically map people's activities,
thereby answering the question: what activities are possible where? Through
Foursquare text only, we obtain a testing accuracy of 59.2% with 10 activity
categories; using additional contextual cues such as venue semantics, we obtain
an increased accuracy of 67.4%. By mapping prior odds of activities via
geographical coordinates, we directly benefit activity recognition systems
built on geo-aware mobile phones.

Can I wash it?: the effect of washing conductive materials used in making
textile based wearable electronic interfaces

We explore the wash-ability of conductive materials used in creating traces
and touch sensors in wearable electronic textiles. We perform a wash test
measuring change in resistivity after each of 10 cycles of washing for
conductive traces constructed using two types of conductive thread, conductive
ink, and combinations of thread and ink.

In this paper we describe a wristwatch-like device using a 3-axis gyro
sensor to determine how a player is strumming the guitar. The device was worn
on the right-handed player's right hand to evaluate the strumming action, which
is important to play the guitar musically in terms of the timing and the
strength of notes. With a newly developed calculation algorithm to specify the
timing and the strength of the motion when the guitar string(s) were strummed,
beginners and experienced players were clearly distinguished without hearing
the sounds. The beginners as well as intermediate-level players showed a fairly
large variation of the maximum angular velocity around the upper arm for each
strum. Since the developed system reports the evaluation results with a
graphical display as well as sound effects in real time, the players may
improve their strumming action without playing back the performance.

An underwater wearable computer for two way human-dolphin communication
experimentation

Research in dolphin cognition and communication in the wild is still a
challenging task for marine biologists. Most problems arise from the
uncontrolled nature of field studies and the challenges of building suitable
underwater research equipment. We present a novel underwater wearable computer
enabling researchers to engage in an audio-based interaction between humans and
dolphins. The design requirements are based on a research protocol developed by
a team of marine biologists associated with the Wild Dolphin Project.